文件列表:

0.TimeSeriesforChicagoTradeData.pdf (8998012, 2023-09-24)
1.EDA_ETS.html (15255948, 2023-09-24)
2.SARIMA.html (10487645, 2023-09-24)
3.ARIMA Error(PreCovid).html (1380920, 2023-09-24)
4.ARIMA Error(PostCovid).html (1329809, 2023-09-24)
5.VAR.html (1151532, 2023-09-24)
raw_data.xlsx (33536, 2023-09-24)

# Time Series Analysis for Chicago's Trade Data This project was completed as part of the MSCA 31006 IP03 Time Series Analysis and Forecasting course in Spring 2023, through the collaborative efforts of Mia Song, Minh Vo, Rolamjaya Hotmartua, Soohyun Iris Lee, and Xiao Pang. ### Objective The goal of prediction for Chicago’s import data is to forecast future import trends accurately, enabling businesses / policymakers to make informed decisions, optimize operations, & drive economic growth ### Data 1. Dataset Size: 254 monthly observations across 9 variables 2. Target Variable: 'Import’ (in $billion) 3. Time Span: January 2002 to February 2023 - Pre-Covid: 2002 - 2018 for forecasting 2019 - Post-Covid: 2002 - 2021 for forecasting 2022 4. Predictor Variables: Supplemented by the following economic indices from the same time period, i.e. - Export (in $billion) - CPI (Consumer Price Index) - PPI (Producer Price Index) - Bond (10-year Government Bond Yield) - Sentiment (Consumer Sentiment Indicator from the University of Michigan) - USDX (US Dollar Index) - Uncertainty (Economic Policy Uncertainty Index for the United States) - Source of Data: United States Census Bureau (Trade Data) and The Fed St. Louis (Other Economic Data)

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